Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523 USA.
Department of Statistics, Colorado State University, Fort Collins, Colorado 80523 USA.
Ecology. 2017 Mar;98(3):632-646. doi: 10.1002/ecy.1674. Epub 2017 Feb 1.
Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.
分析生态数据通常需要对空间和时间过程产生的自相关进行建模。许多用于解释自相关的看似不同的统计方法都可以表示为包含基函数的回归模型。基函数还使生态学家能够修改广泛的现有生态模型，以解释自相关，这可以提高推理和预测精度。此外，了解基函数的性质对于评估空间或时间序列模型的拟合度、检测隐藏的共线性形式以及分析大数据集至关重要。我们介绍了与基函数相关的重要概念和性质，并说明了生态学家在对生态数据中的自相关进行建模时可以使用的几种工具和技术。
